| Summary: | 碩士 === 國立臺灣大學 === 資訊管理學研究所 === 107 === Modeling volatility becomes crucial in financial applications ranging from risk management, asset allocation to option pricing. Stochastic volatility (SV) models are one of the volatility models that treat the variances as an unobserved component following a stochastic process.
In this paper, we focus on the discrete time stochastic volatility (SV) models. We provide an R package, logsv, which implements the basic log SV model with the estimation of the Markov chain Monte Carlo approach and disclose all the implementation details. We fit the model to simulated datasets and real world datasets to test the fitness and correctness of our implementation. The experiment results with all datasets show that the estimation procedure works well on both parameter and volatility estimation. With the estimation results of the basic log SV model, we discuss some period of high volatility and highlight the financial crises and events that are potentially related.
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